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1. Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse

   https://doi.org/10.5194/acp-25-3785-2025  

   Yang, Fan; Sadi, Hamed Fahandezh; Shaw, Raymond A; Hoffmann, Fabian; Hou, Pei; Wang, Aaron; Ovchinnikov, Mikhail (January 2025, Atmospheric Chemistry and Physics)

   Abstract. It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions. 

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2. Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface

   https://doi.org/10.1126/sciadv.adl5044  

   Su, Tianning; Li, Zhanqing; Henao, Natalia Roldan; Luan, Qingzu; Yu, Fangqun (May 2024, Science Advances)

   Aerosol-cloud interactions (ACIs) are vital for regulating Earth’s climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates. 

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3. Quantifying the Influence of Cloud Radiative Feedbacks on Arctic Surface Warming Using Cloud Locking in an Earth System Model

   https://doi.org/10.1029/2020GL089207  

   Middlemas, E. A.; Kay, J. E.; Medeiros, B. M.; Maroon, E. A. (August 2020, Geophysical Research Letters)

   Abstract Understanding the influence of clouds on amplified Arctic surface warming remains an important unsolved research problem. Here, this cloud influence is directly quantified by disabling cloud radiative feedbacks or “cloud locking” within a state‐of‐the‐art and well‐documented model. Through comparison of idealized greenhouse warming experiments with and without cloud locking, the influence of Arctic and global cloud feedbacks is assessed. Global cloud feedbacks increase both global and Arctic warming by around 25%. In contrast, disabling Arctic cloud feedbacks has a negligible influence on both Arctic and global surface warming. Interestingly, the sum of noncloud radiative feedbacks does not change with either global or Arctic‐only cloud locking. Notably, the influence of Arctic cloud feedbacks is likely underestimated, because, like many models, the model used here underestimates high‐latitude supercooled cloud liquid. More broadly, this work demonstrates the value of regional and global cloud locking in a well‐characterized model. 

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4. Testing Cloud Applications under Cloud-Uncertainty Performance Effects

   Wang, Wei; Tian, Ningjing; Huang, Sunzhou; He, Sen; Srivastava, Abhijeet; Soffa, Mary Lou; Pollock, Lori (April 2018, the IEEE Conference on Software Testing, Validation and Verification (ICST))

   The paradigm shift of deploying applications to the cloud has introduced both opportunities and challenges. Although clouds use elasticity to scale resource usage at runtime to help meet an application’s performance requirements, developers are still challenged by unpredictable performance, little control of execution environment, and differences among cloud service providers, all while being charged for their cloud usages. Application performance stability is particularly affected by multi-tenancy in which the hardware is shared among varying applications and virtual machines. Developers porting their applications need to meet performance requirements, but testing on the cloud under the effects of performance uncertainty is difficult and expensive, due to high cloud usage costs. This paper presents a first approach to testing an application with typical inputs for how its performance will be affected by performance uncertainty, without incurring undue costs of bruteforce testing in the cloud. We specify cloud uncertainty testing criteria, design a test-based strategy to characterize the blackbox cloud’s performance distributions using these testing criteria, and support execution of tests to characterize the resource usage and cloud baseline performance of the application to be deployed. Importantly, we developed a smart test oracle that estimates the application’s performance with certain confidence levels using the above characterization test results and determines whether it will meet its performance requirements. We evaluated our testing approach on both the Chameleon cloud and Amazon web services; results indicate that this testing strategy shows promise as a cost-effective approach to test for performance effects of cloud uncertainty when porting an application to the cloud. 

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5. Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

   https://doi.org/10.5194/amt-13-5459-2020  

   Marais, Willem J.; Holz, Robert E.; Reid, Jeffrey S.; Willett, Rebecca M. (January 2020, Atmospheric Measurement Techniques)

   null (Ed.)

   Abstract. Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products. 

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